A Network-Enabled Myoelectric Platform for Prototyping Research Outside of the Lab

Dyson, Matthew; Olsen, Jennifer; Dupan, Sigrid

doi:10.1109/embc46164.2021.9630318

Cited by 3 publications

(6 citation statements)

References 19 publications

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“…Laboratory-based training must accommodate all learning rates which is sub-optimal for many other participants and the experimental operators. Home-based training has numerous advantages in this area [48]. We anticipated that, given time, most participants would be able to achieve high levels of performance.…”

Section: Discussionmentioning

confidence: 99%

“…Experiment 2 was carried out in the participants' homes. Participants were provided with a networkenabled Arduino-based EMG acquisition device, a description of which may be found in [48].…”

Section: Experiments 2: Home-based Trainingmentioning

confidence: 99%

“…Two channels of surface EMG were acquired using modified Gravity analog EMG sensors (OYMotion Technologies Co. Ltd Shanghai, China) [48]. Each modified Gravity analog EMG sensor had an integrated ground electrode [48]. Signals were acquired at 500 Hz and band-pass filtered between 20 Hz and 150 Hz.…”

Section: Estimation Of Muscle Activitymentioning

confidence: 99%

“…Pythonbased experimental software was installed on laptop devices for use in the home or on the participants' own devices. All data were automatically uploaded to a remote server for retrieval and analysis [48].…”

Section: Recordingsmentioning

confidence: 99%

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Delaying feedback during pre-device training facilitates the retention of novel myoelectric skills: a laboratory and home-based study

et al. 2023

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Objective. The objective of this study was to assess the impact of delayed feedback training on the retention of novel myoelectric skills, and to demonstrate the use of this training approach in the home environment. Approach. We trained limb-intact participants to use a motor learning-based upper-limb prosthesis control scheme called abstract decoding. A delayed feedback paradigm intended to prevent within-trial adaptation and to facilitate motor learning was used. We conducted two multi-day experiments. Experiment 1 was a laboratory-based study consisting of two groups trained over a four-day period with concurrent or delayed feedback. An additional follow-up session took place after 18 days to assess the retention of motor skills. Experiment 2 was a home-based pilot study that took place over five consecutive days to investigate delayed feedback performance when using bespoke training structures. Main Results. Approximately 35,000 trials were collected across both experiments. Experiment 1 found that the retention of motor skills for the delayed feedback group was significantly better than that of their concurrent feedback counterparts. In addition, the delayed feedback group improved their retention of motor skills across days, whereas the concurrent feedback group did not. Experiment 2 demonstrated that by using a bespoke training protocol in an environment that is more conducive to learning, it is possible for participants to become highly accurate in the absence of feedback. Significance. These results show that with delayed feedback training, it is possible to retain novel myoelectric skills. Using abstract decoding participants can activate four distinct muscle patterns without using complex algorithms. The accuracy achieved in the pilot study supports the feasibility of motor learning-based upper-limb prosthesis control after home-based myoelectric training.

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Section: Discussionmentioning

confidence: 99%

Section: Experiments 2: Home-based Trainingmentioning

confidence: 99%

Section: Estimation Of Muscle Activitymentioning

confidence: 99%

Section: Recordingsmentioning

confidence: 99%

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Delaying feedback during pre-device training facilitates the retention of novel myoelectric skills: a laboratory and home-based study

et al. 2023

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“…A different approach from the demonstration is to self-host an open-source solution for data recording and user interfacing, e.g. [53]. The server has to be developed from sketch, but it is more flexible in the short run.…”

Section: Discussionmentioning

confidence: 99%

Internet of Things for beyond-the-laboratory prosthetics research

Dyson

Nazarpour

2022

Phil. Trans. R. Soc. A.

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Research on upper-limb prostheses is typically laboratory-based. Evidence indicates that research has not yet led to prostheses that meet user needs. Inefficient communication loops between users, clinicians and manufacturers limit the amount of quantitative and qualitative data that researchers can use in refining their innovations. This paper offers a first demonstration of an alternative paradigm by which remote, beyond-the-laboratory prosthesis research according to user needs is feasible. Specifically, the proposed Internet of Things setting allows remote data collection, real-time visualization and prosthesis reprogramming through Wi-Fi and a commercial cloud portal. Via a dashboard, the user can adjust the configuration of the device and append contextual information to the prosthetic data. We evaluated this demonstrator in real-time experiments with three able-bodied participants. Results promise the potential of contextual data collection and system update through the internet, which may provide real-life data for algorithm training and reduce the complexity of send-home trials. This article is part of the theme issue ‘Advanced neurotechnologies: translating innovation for health and well-being’.

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